diff --git a/src/mlrose_ky/__init__.py b/src/mlrose_ky/__init__.py
index bed2fab7..bdf93ca4 100644
--- a/src/mlrose_ky/__init__.py
+++ b/src/mlrose_ky/__init__.py
@@ -25,26 +25,15 @@
 from .algorithms.decay import GeomDecay, ArithDecay, ExpDecay, CustomSchedule
 
 # noinspection PyUnresolvedReferences
-from .algorithms.crossovers import OnePointCrossover, UniformCrossover, TSPCrossover
+from .algorithms.crossovers import OnePointCrossOver, UniformCrossOver, TSPCrossOver
 
 # noinspection PyUnresolvedReferences
 from .algorithms.mutators import ChangeOneMutator, DiscreteMutator, SwapMutator, ShiftOneMutator
 
 # noinspection PyUnresolvedReferences
-from .fitness import (
-    OneMax,
-    FlipFlop,
-    FourPeaks,
-    SixPeaks,
-    ContinuousPeaks,
-    Knapsack,
-    TravellingSales,
-    Queens,
-    MaxKColor,
-    CustomFitness,
-)
+from .fitness import OneMax, FlipFlop, FourPeaks, SixPeaks, ContinuousPeaks, Knapsack, TravellingSales, Queens, MaxKColor, CustomFitness
 
-# noinspection PyUnresolvedReferences
+# noinspection PyUnresolvedReferences,PyProtectedMember
 from .neural import NeuralNetwork, LinearRegression, LogisticRegression, NNClassifier, _nn_core
 
 # noinspection PyUnresolvedReferences
diff --git a/src/mlrose_ky/algorithms/__init__.py b/src/mlrose_ky/algorithms/__init__.py
index 0923ccb8..ced6237b 100644
--- a/src/mlrose_ky/algorithms/__init__.py
+++ b/src/mlrose_ky/algorithms/__init__.py
@@ -3,12 +3,15 @@
 # Authors: Genevieve Hayes (modified by Andrew Rollings, Kyle Nakamura)
 # License: BSD 3-clause
 
-from .crossovers import UniformCrossover, TSPCrossover, OnePointCrossover
-from .decay import ArithDecay, CustomSchedule, ExpDecay, GeomDecay
 from .ga import genetic_alg
 from .gd import gradient_descent
 from .hc import hill_climb
 from .mimic import mimic
-from .mutators import ChangeOneMutator, DiscreteMutator, ShiftOneMutator, SwapMutator
 from .rhc import random_hill_climb
 from .sa import simulated_annealing
+
+from .crossovers import UniformCrossOver, TSPCrossOver, OnePointCrossOver
+
+from .decay import ArithDecay, CustomSchedule, ExpDecay, GeomDecay
+
+from .mutators import ChangeOneMutator, DiscreteMutator, ShiftOneMutator, SwapMutator
diff --git a/src/mlrose_ky/algorithms/crossovers/__init__.py b/src/mlrose_ky/algorithms/crossovers/__init__.py
index 63b3e28d..4a19eeb5 100644
--- a/src/mlrose_ky/algorithms/crossovers/__init__.py
+++ b/src/mlrose_ky/algorithms/crossovers/__init__.py
@@ -3,6 +3,6 @@
 # Author: Genevieve Hayes
 # License: BSD 3-clause
 
-from .one_point_crossover import OnePointCrossover
-from .tsp_crossover import TSPCrossover
-from .uniform_crossover import UniformCrossover
+from .one_point_crossover import OnePointCrossOver
+from .tsp_crossover import TSPCrossOver
+from .uniform_crossover import UniformCrossOver
diff --git a/src/mlrose_ky/algorithms/crossovers/_crossover_base.py b/src/mlrose_ky/algorithms/crossovers/_crossover_base.py
index 5ea36553..19064fd5 100644
--- a/src/mlrose_ky/algorithms/crossovers/_crossover_base.py
+++ b/src/mlrose_ky/algorithms/crossovers/_crossover_base.py
@@ -8,10 +8,12 @@
 # License: BSD 3-clause
 
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Any, Sequence
 
+import numpy as np
 
-class _CrossoverBase(ABC):
+
+class _CrossOverBase(ABC):
     """
     Base class for crossover operations in a genetic algorithm.
 
@@ -20,7 +22,7 @@ class _CrossoverBase(ABC):
 
     Parameters
     ----------
-    optimization_problem : Any
+    opt_prob : Any
         An instance of the optimization problem related to the genetic algorithm.
         This problem instance should provide necessary properties like 'length'
         that might be needed for the crossover operation.
@@ -34,21 +36,21 @@ class _CrossoverBase(ABC):
         'length' property.
     """
 
-    def __init__(self, optimization_problem: Any):
+    def __init__(self, opt_prob: Any):
         """
-        Initialize the CrossoverBase with the given optimization problem.
+        Initialize the _CrossOverBase with the given optimization problem.
 
         Parameters
         ----------
-        optimization_problem : Any
+        opt_prob : Any
             An instance of the optimization problem related to the GA.
         """
         super().__init__()
-        self._opt_prob: Any = optimization_problem
-        self._length: int = optimization_problem.length
+        self._opt_prob: Any = opt_prob
+        self._length: int = opt_prob.length
 
     @abstractmethod
-    def mate(self, parent1: Any, parent2: Any) -> Any:
+    def mate(self, p1: Sequence[int | float], p2: Sequence[int | float]) -> np.ndarray:
         """
         Perform the crossover (mating) between two parents to produce offspring.
 
@@ -57,14 +59,14 @@ def mate(self, parent1: Any, parent2: Any) -> Any:
 
         Parameters
         ----------
-        parent1 : Any
+        p1 : Sequence[int | float]
             The first parent participating in the crossover.
-        parent2 : Any
+        p2 : Sequence[int | float]
             The second parent participating in the crossover.
 
         Returns
         -------
-        Any
+        np.ndarray
             The offspring resulting from the crossover. The type of this result
             can vary depending on the specific GA implementation.
         """
diff --git a/src/mlrose_ky/algorithms/crossovers/one_point_crossover.py b/src/mlrose_ky/algorithms/crossovers/one_point_crossover.py
index ab498edf..4a7b787c 100644
--- a/src/mlrose_ky/algorithms/crossovers/one_point_crossover.py
+++ b/src/mlrose_ky/algorithms/crossovers/one_point_crossover.py
@@ -11,10 +11,10 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers._crossover_base import _CrossoverBase
+from mlrose_ky.algorithms.crossovers._crossover_base import _CrossOverBase
 
 
-class OnePointCrossover(_CrossoverBase):
+class OnePointCrossOver(_CrossOverBase):
     """
     One-point crossover for genetic algorithms.
 
@@ -23,21 +23,21 @@ class OnePointCrossover(_CrossoverBase):
     is exchanged to create a new offspring.
 
     Inherits from:
-    _CrossoverBase : Abstract base class for crossover operations.
+    _CrossOverBase : Abstract base class for crossover operations.
     """
 
-    def __init__(self, optimization_problem: Any):
+    def __init__(self, opt_prob: Any):
         """
-        Initialize the OnePointCrossover with the given optimization problem.
+        Initialize the OnePointCrossOver with the given optimization problem.
 
         Parameters
         ----------
-        optimization_problem : Any
+        opt_prob : Any
             An instance of the optimization problem related to the genetic algorithm.
         """
-        super().__init__(optimization_problem)
+        super().__init__(opt_prob)
 
-    def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray:
+    def mate(self, p1: Sequence[float], p2: Sequence[float]) -> np.ndarray:
         """
         Perform the one-point crossover between two parent sequences to produce offspring.
 
@@ -46,9 +46,9 @@ def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray
 
         Parameters
         ----------
-        parent1 : Sequence[float]
+        p1 : Sequence[float]
             The first parent chromosome sequence.
-        parent2 : Sequence[float]
+        p2 : Sequence[float]
             The second parent chromosome sequence.
 
         Returns
@@ -57,4 +57,4 @@ def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray
             The offspring chromosome resulting from the crossover.
         """
         crossover_point = 1 + np.random.randint(self._length - 1)
-        return np.array([*parent1[:crossover_point], *parent2[crossover_point:]])
+        return np.array([*p1[:crossover_point], *p2[crossover_point:]])
diff --git a/src/mlrose_ky/algorithms/crossovers/tsp_crossover.py b/src/mlrose_ky/algorithms/crossovers/tsp_crossover.py
index e29b9c5f..4cb5126e 100644
--- a/src/mlrose_ky/algorithms/crossovers/tsp_crossover.py
+++ b/src/mlrose_ky/algorithms/crossovers/tsp_crossover.py
@@ -12,10 +12,10 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers._crossover_base import _CrossoverBase
+from mlrose_ky.algorithms.crossovers._crossover_base import _CrossOverBase
 
 
-class TSPCrossover(_CrossoverBase):
+class TSPCrossOver(_CrossOverBase):
     """
     Crossover operation tailored for the Travelling Salesperson Problem (TSP) in genetic algorithms.
 
@@ -24,21 +24,21 @@ class TSPCrossover(_CrossoverBase):
     logic to combine parental genes.
 
     Inherits from:
-    _CrossoverBase : Abstract base class for crossover operations.
+    _CrossOverBase : Abstract base class for crossover operations.
     """
 
-    def __init__(self, optimization_problem: Any):
+    def __init__(self, opt_prob: Any):
         """
-        Initialize the TSPCrossover with the given optimization problem.
+        Initialize the TSPCrossOver with the given optimization problem.
 
         Parameters
         ----------
-        optimization_problem : Any
+        opt_prob : Any
             An instance of the optimization problem related to the genetic algorithm.
         """
-        super().__init__(optimization_problem)
+        super().__init__(opt_prob)
 
-    def mate(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.ndarray:
+    def mate(self, p1: Sequence[int], p2: Sequence[int]) -> np.ndarray:
         """
         Perform the crossover (mating) between two parent sequences to produce offspring.
 
@@ -47,9 +47,9 @@ def mate(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.ndarray:
 
         Parameters
         ----------
-        parent1 : Sequence[int]
+        p1 : Sequence[int]
             The first parent representing a TSP route.
-        parent2 : Sequence[int]
+        p2 : Sequence[int]
             The second parent representing a TSP route.
 
         Returns
@@ -57,18 +57,18 @@ def mate(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.ndarray:
         np.ndarray
             The offspring representing a new TSP route.
         """
-        return self._mate_fill(parent1, parent2)
+        return self._mate_fill(p1, p2)
 
-    def _mate_fill(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.ndarray:
+    def _mate_fill(self, p1: Sequence[int], p2: Sequence[int]) -> np.ndarray:
         """
         Perform a fill-based crossover using a segment of the first parent and filling
         the rest with non-repeated cities from the second parent.
 
         Parameters
         ----------
-        parent1 : Sequence[int]
+        p1 : Sequence[int]
             The first parent representing a TSP route.
-        parent2 : Sequence[int]
+        p2 : Sequence[int]
             The second parent representing a TSP route.
 
         Returns
@@ -79,15 +79,15 @@ def _mate_fill(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.ndarr
         if self._length > 1:
             n = 1 + np.random.randint(self._length - 1)
             child = np.array([0] * self._length)
-            child[:n] = parent1[:n]
-            unvisited = [city for city in parent2 if city not in parent1[:n]]
+            child[:n] = p1[:n]
+            unvisited = [city for city in p2 if city not in p1[:n]]
             child[n:] = unvisited
         else:
-            child = np.copy(parent1 if np.random.randint(2) == 0 else parent2)
+            child = np.copy(p1 if np.random.randint(2) == 0 else p2)
 
         return child
 
-    def _mate_traverse(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.ndarray:
+    def _mate_traverse(self, parent_1: Sequence[int], parent_2: Sequence[int]) -> np.ndarray:
         """
         Perform a traversal-based crossover using city adjacency considerations from
         both parents to construct a viable TSP route.
@@ -98,9 +98,9 @@ def _mate_traverse(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.n
 
         Parameters
         ----------
-        parent1 : Sequence[int]
+        parent_1 : Sequence[int]
             The first parent representing a TSP route.
-        parent2 : Sequence[int]
+        parent_2 : Sequence[int]
             The second parent representing a TSP route.
 
         Returns
@@ -109,13 +109,13 @@ def _mate_traverse(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.n
             The offspring TSP route.
         """
         if self._length > 1:
-            next_city_parent1 = np.append(parent1[1:], parent1[0])
-            next_city_parent2 = np.append(parent2[1:], parent2[0])
+            next_city_parent1 = np.append(parent_1[1:], parent_1[0])
+            next_city_parent2 = np.append(parent_2[1:], parent_2[0])
 
             visited_cities = [False] * self._length
             offspring_route = np.array([0] * self._length)
 
-            starting_city = np.random.randint(len(parent1))
+            starting_city = np.random.randint(len(parent_1))
             offspring_route[0] = starting_city
             visited_cities[starting_city] = True
 
@@ -137,13 +137,13 @@ def _mate_traverse(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.n
                     next_city = next_city2 if fitness1 > fitness2 else next_city1  # Choose the smaller distance
                 else:
                     while True:
-                        next_city = np.random.randint(len(parent1))
+                        next_city = np.random.randint(len(parent_1))
                         if not visited_cities[next_city]:
                             break
 
                 offspring_route[index] = next_city
                 visited_cities[next_city] = True
         else:
-            offspring_route = np.copy(parent1 if np.random.randint(2) == 0 else parent2)
+            offspring_route = np.copy(parent_1 if np.random.randint(2) == 0 else parent_2)
 
         return offspring_route
diff --git a/src/mlrose_ky/algorithms/crossovers/uniform_crossover.py b/src/mlrose_ky/algorithms/crossovers/uniform_crossover.py
index 5c7f247e..6da597a3 100644
--- a/src/mlrose_ky/algorithms/crossovers/uniform_crossover.py
+++ b/src/mlrose_ky/algorithms/crossovers/uniform_crossover.py
@@ -12,10 +12,10 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers._crossover_base import _CrossoverBase
+from mlrose_ky.algorithms.crossovers._crossover_base import _CrossOverBase
 
 
-class UniformCrossover(_CrossoverBase):
+class UniformCrossOver(_CrossOverBase):
     """
     Uniform crossover for genetic algorithms.
 
@@ -24,21 +24,21 @@ class UniformCrossover(_CrossoverBase):
     method is often used when no prior knowledge about the problem structure is known.
 
     Inherits from:
-    _CrossoverBase : Abstract base class for crossover operations.
+    _CrossOverBase : Abstract base class for crossover operations.
     """
 
-    def __init__(self, optimization_problem: Any):
+    def __init__(self, opt_prob: Any):
         """
-        Initialize the UniformCrossover with the given optimization problem.
+        Initialize the UniformCrossOver with the given optimization problem.
 
         Parameters
         ----------
-        optimization_problem : Any
+        opt_prob : Any
             An instance of the optimization problem related to the genetic algorithm.
         """
-        super().__init__(optimization_problem)
+        super().__init__(opt_prob)
 
-    def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray:
+    def mate(self, p1: Sequence[float], p2: Sequence[float]) -> np.ndarray:
         """
         Perform the uniform crossover between two parent sequences to produce offspring.
 
@@ -47,9 +47,9 @@ def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray
 
         Parameters
         ----------
-        parent1 : Sequence[float]
+        p1 : Sequence[float]
             The first parent chromosome sequence.
-        parent2 : Sequence[float]
+        p2 : Sequence[float]
             The second parent chromosome sequence.
 
         Returns
@@ -58,5 +58,5 @@ def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray
             The offspring chromosome resulting from the crossover.
         """
         gene_selector = np.random.randint(2, size=self._length)
-        stacked_parents = np.vstack((parent1, parent2))
+        stacked_parents = np.vstack((p1, p2))
         return stacked_parents[gene_selector, np.arange(self._length)]
diff --git a/src/mlrose_ky/algorithms/decay/__init__.py b/src/mlrose_ky/algorithms/decay/__init__.py
index 32ab6dbe..bcb149b4 100644
--- a/src/mlrose_ky/algorithms/decay/__init__.py
+++ b/src/mlrose_ky/algorithms/decay/__init__.py
@@ -3,7 +3,7 @@
 # Author: Genevieve Hayes
 # License: BSD 3-clause
 
-from .arithmetic_decay import ArithDecay
-from .custom_decay import CustomSchedule
-from .exponential_decay import ExpDecay
-from .geometric_decay import GeomDecay
+from .arith_decay import ArithDecay
+from .custom_schedule import CustomSchedule
+from .expl_decay import ExpDecay
+from .geom_decay import GeomDecay
diff --git a/src/mlrose_ky/algorithms/decay/arithmetic_decay.py b/src/mlrose_ky/algorithms/decay/arith_decay.py
similarity index 93%
rename from src/mlrose_ky/algorithms/decay/arithmetic_decay.py
rename to src/mlrose_ky/algorithms/decay/arith_decay.py
index 433fd5b8..25900bc7 100644
--- a/src/mlrose_ky/algorithms/decay/arithmetic_decay.py
+++ b/src/mlrose_ky/algorithms/decay/arith_decay.py
@@ -64,13 +64,13 @@ def __eq__(self, other: object) -> bool:
             return False
         return self.init_temp == other.init_temp and self.decay == other.decay and self.min_temp == other.min_temp
 
-    def evaluate(self, time: int) -> float:
+    def evaluate(self, t: int) -> float:
         """
         Calculate and return the temperature parameter at the given time.
 
         Parameters
         ----------
-        time : int
+        t : int
             The time at which to evaluate the temperature parameter.
 
         Returns
@@ -78,15 +78,15 @@ def evaluate(self, time: int) -> float:
         float
             The temperature parameter at the given time, respecting the minimum temperature.
         """
-        return max(self.init_temp - (self.decay * time), self.min_temp)
+        return max(self.init_temp - (self.decay * t), self.min_temp)
 
-    def get_info(self, time: int = None, prefix: str = "") -> dict:
+    def get_info__(self, t: int = None, prefix: str = "") -> dict:
         """
         Generate a dictionary containing the decay schedule's settings and optionally its current value.
 
         Parameters
         ----------
-        time : int, optional
+        t : int, optional
             The time at which to evaluate the current temperature value.
             If provided, the current value is included in the returned dictionary.
         prefix : str, optional
@@ -106,7 +106,7 @@ def get_info(self, time: int = None, prefix: str = "") -> dict:
             f"{info_prefix}min_temp": self.min_temp,
         }
 
-        if time is not None:
-            info[f"{info_prefix}current_value"] = self.evaluate(time)
+        if t is not None:
+            info[f"{info_prefix}current_value"] = self.evaluate(t)
 
         return info
diff --git a/src/mlrose_ky/algorithms/decay/custom_decay.py b/src/mlrose_ky/algorithms/decay/custom_schedule.py
similarity index 100%
rename from src/mlrose_ky/algorithms/decay/custom_decay.py
rename to src/mlrose_ky/algorithms/decay/custom_schedule.py
diff --git a/src/mlrose_ky/algorithms/decay/exponential_decay.py b/src/mlrose_ky/algorithms/decay/expl_decay.py
similarity index 99%
rename from src/mlrose_ky/algorithms/decay/exponential_decay.py
rename to src/mlrose_ky/algorithms/decay/expl_decay.py
index 0eec1db0..65f48c86 100644
--- a/src/mlrose_ky/algorithms/decay/exponential_decay.py
+++ b/src/mlrose_ky/algorithms/decay/expl_decay.py
@@ -6,9 +6,6 @@
 import numpy as np
 
 
-# temp comment
-
-
 class ExpDecay:
     """
     Defines an exponential decay schedule for the temperature parameter T in simulated annealing,
diff --git a/src/mlrose_ky/algorithms/decay/geometric_decay.py b/src/mlrose_ky/algorithms/decay/geom_decay.py
similarity index 100%
rename from src/mlrose_ky/algorithms/decay/geometric_decay.py
rename to src/mlrose_ky/algorithms/decay/geom_decay.py
diff --git a/src/mlrose_ky/algorithms/ga.py b/src/mlrose_ky/algorithms/ga.py
index 3e84694b..e2f3f6fd 100644
--- a/src/mlrose_ky/algorithms/ga.py
+++ b/src/mlrose_ky/algorithms/ga.py
@@ -110,7 +110,7 @@ def genetic_alg(
     max_iters: int | float = np.inf,
     curve: bool = False,
     random_state: int = None,
-    state_fitness_callback: Callable[..., Any] = None,
+    state_fitness_callback: Callable = None,
     callback_user_info: Any = None,
     hamming_factor: float = 0.0,
     hamming_decay_factor: float = None,
diff --git a/src/mlrose_ky/algorithms/hc.py b/src/mlrose_ky/algorithms/hc.py
index 1ff8a1a7..267e2cba 100644
--- a/src/mlrose_ky/algorithms/hc.py
+++ b/src/mlrose_ky/algorithms/hc.py
@@ -13,7 +13,7 @@
 @short_name("hc")
 def hill_climb(
     problem: Any,
-    max_iters: int = np.inf,
+    max_iters: int | float = np.inf,
     restarts: int = 0,
     init_state: np.ndarray = None,
     curve: bool = False,
diff --git a/src/mlrose_ky/algorithms/mimic.py b/src/mlrose_ky/algorithms/mimic.py
index ee8ddbf3..31b6459d 100644
--- a/src/mlrose_ky/algorithms/mimic.py
+++ b/src/mlrose_ky/algorithms/mimic.py
@@ -17,7 +17,7 @@ def mimic(
     keep_pct: float = 0.2,
     max_attempts: int = 10,
     noise: float = 0.0,
-    max_iters: int = np.inf,
+    max_iters: int | float = np.inf,
     curve: bool = False,
     random_state: int = None,
     state_fitness_callback: Callable = None,
diff --git a/src/mlrose_ky/algorithms/mutators/__init__.py b/src/mlrose_ky/algorithms/mutators/__init__.py
index e5ef3892..0bf03ef6 100644
--- a/src/mlrose_ky/algorithms/mutators/__init__.py
+++ b/src/mlrose_ky/algorithms/mutators/__init__.py
@@ -3,7 +3,7 @@
 # Authors: Genevieve Hayes (modified by Andrew Rollings, Kyle Nakamura)
 # License: BSD 3-clause
 
+from .change_one_mutator import ChangeOneMutator
 from .discrete_mutator import DiscreteMutator
-from .gene_swap_mutator import SwapMutator
-from .single_gene_mutator import ChangeOneMutator
-from .single_shift_mutator import ShiftOneMutator
+from .shift_one_mutator import ShiftOneMutator
+from .swap_mutator import SwapMutator
diff --git a/src/mlrose_ky/algorithms/mutators/_mutator_base.py b/src/mlrose_ky/algorithms/mutators/_mutator_base.py
index 9b3aaeec..2e24b026 100644
--- a/src/mlrose_ky/algorithms/mutators/_mutator_base.py
+++ b/src/mlrose_ky/algorithms/mutators/_mutator_base.py
@@ -40,7 +40,7 @@ def __init__(self, opt_prob: Any):
         self._length: int = opt_prob.length
 
     @abstractmethod
-    def mutate(self, child: np.ndarray, mutation_probability: float) -> Any:
+    def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
         """
         Apply mutation operation to a given child chromosome based on a mutation probability.
 
diff --git a/src/mlrose_ky/algorithms/mutators/single_gene_mutator.py b/src/mlrose_ky/algorithms/mutators/change_one_mutator.py
similarity index 100%
rename from src/mlrose_ky/algorithms/mutators/single_gene_mutator.py
rename to src/mlrose_ky/algorithms/mutators/change_one_mutator.py
diff --git a/src/mlrose_ky/algorithms/mutators/single_shift_mutator.py b/src/mlrose_ky/algorithms/mutators/shift_one_mutator.py
similarity index 100%
rename from src/mlrose_ky/algorithms/mutators/single_shift_mutator.py
rename to src/mlrose_ky/algorithms/mutators/shift_one_mutator.py
diff --git a/src/mlrose_ky/algorithms/mutators/gene_swap_mutator.py b/src/mlrose_ky/algorithms/mutators/swap_mutator.py
similarity index 100%
rename from src/mlrose_ky/algorithms/mutators/gene_swap_mutator.py
rename to src/mlrose_ky/algorithms/mutators/swap_mutator.py
diff --git a/src/mlrose_ky/algorithms/rhc.py b/src/mlrose_ky/algorithms/rhc.py
index 7ec54473..38f53da3 100644
--- a/src/mlrose_ky/algorithms/rhc.py
+++ b/src/mlrose_ky/algorithms/rhc.py
@@ -14,7 +14,7 @@
 def random_hill_climb(
     problem: Any,
     max_attempts: int = 10,
-    max_iters: int = np.inf,
+    max_iters: int | float = np.inf,
     restarts: int = 0,
     init_state: np.ndarray = None,
     curve: bool = False,
diff --git a/src/mlrose_ky/algorithms/sa.py b/src/mlrose_ky/algorithms/sa.py
index 91e83667..bccd948a 100644
--- a/src/mlrose_ky/algorithms/sa.py
+++ b/src/mlrose_ky/algorithms/sa.py
@@ -16,7 +16,7 @@ def simulated_annealing(
     problem: Any,
     schedule: Any = GeomDecay(),
     max_attempts: int = 10,
-    max_iters: int = np.inf,
+    max_iters: int | float = np.inf,
     init_state: np.ndarray = None,
     curve: bool = False,
     random_state: int = None,
diff --git a/src/mlrose_ky/decorators/short_name_decorator.py b/src/mlrose_ky/decorators/short_name_decorator.py
index 1213406b..d561e39e 100644
--- a/src/mlrose_ky/decorators/short_name_decorator.py
+++ b/src/mlrose_ky/decorators/short_name_decorator.py
@@ -29,12 +29,12 @@ def short_name(expr: str) -> Callable:
         A decorator that assigns the provided short name to a function and returns the function.
     """
 
-    def decorator(func: Callable) -> Callable:
+    def short_name_func_applicator(func: Callable) -> Callable:
         """Assign a short name to the given function."""
         func.__short_name__ = expr
         return func
 
-    return decorator
+    return short_name_func_applicator
 
 
 def get_short_name(v: Any) -> str:
diff --git a/src/mlrose_ky/fitness/__init__.py b/src/mlrose_ky/fitness/__init__.py
index 19946004..e79e84f0 100644
--- a/src/mlrose_ky/fitness/__init__.py
+++ b/src/mlrose_ky/fitness/__init__.py
@@ -1,7 +1,6 @@
 """Classes for defining fitness functions (i.e., optimization problems) for optimization algorithms."""
 
 from .continuous_peaks import ContinuousPeaks
-from .custom_fitness import CustomFitness
 from .flip_flop import FlipFlop
 from .four_peaks import FourPeaks
 from .knapsack import Knapsack
@@ -9,4 +8,6 @@
 from .one_max import OneMax
 from .queens import Queens
 from .six_peaks import SixPeaks
-from .travelling_salesperson import TravellingSales
+from .travelling_sales import TravellingSales
+
+from .custom_fitness import CustomFitness
diff --git a/src/mlrose_ky/fitness/continuous_peaks.py b/src/mlrose_ky/fitness/continuous_peaks.py
index c1eb91fc..d1275278 100644
--- a/src/mlrose_ky/fitness/continuous_peaks.py
+++ b/src/mlrose_ky/fitness/continuous_peaks.py
@@ -38,8 +38,8 @@ class ContinuousPeaks:
     """
 
     def __init__(self, t_pct: float = 0.1):
-        self.t_pct: float = t_pct
         self.prob_type: str = "discrete"
+        self.t_pct: float = t_pct
 
         if not (0 <= self.t_pct <= 1):
             raise ValueError(f"t_pct must be between 0 and 1, got {self.t_pct} instead.")
diff --git a/src/mlrose_ky/fitness/custom_fitness.py b/src/mlrose_ky/fitness/custom_fitness.py
index c30b7dfb..c44850e7 100644
--- a/src/mlrose_ky/fitness/custom_fitness.py
+++ b/src/mlrose_ky/fitness/custom_fitness.py
@@ -59,8 +59,8 @@ def __init__(self, fitness_fn: Callable[..., float], problem_type: str = "either
         if problem_type not in ["discrete", "continuous", "tsp", "either"]:
             raise ValueError(f"Invalid problem_type: {problem_type}. Must be one of ['discrete', 'continuous', 'tsp', 'either'].")
 
-        self.fitness_fn: Callable[..., float] = fitness_fn
         self.problem_type: str = problem_type
+        self.fitness_fn: Callable[..., float] = fitness_fn
         self.kwargs: Any = kwargs
 
     def evaluate(self, state: np.ndarray) -> float:
diff --git a/src/mlrose_ky/fitness/four_peaks.py b/src/mlrose_ky/fitness/four_peaks.py
index b005cce6..86577a7b 100644
--- a/src/mlrose_ky/fitness/four_peaks.py
+++ b/src/mlrose_ky/fitness/four_peaks.py
@@ -58,8 +58,8 @@ def __init__(self, t_pct: float = 0.1):
         t_pct : float, optional, default=0.1
             Threshold parameter (T) for Four Peaks fitness function.
         """
-        self.t_pct: float = t_pct
         self.prob_type: str = "discrete"
+        self.t_pct: float = t_pct
 
         if not (0 <= self.t_pct <= 1):
             raise ValueError(f"threshold_pct must be between 0 and 1, got {self.t_pct}.")
diff --git a/src/mlrose_ky/fitness/six_peaks.py b/src/mlrose_ky/fitness/six_peaks.py
index ddf94c7f..4b9daceb 100644
--- a/src/mlrose_ky/fitness/six_peaks.py
+++ b/src/mlrose_ky/fitness/six_peaks.py
@@ -59,8 +59,8 @@ def __init__(self, t_pct: float = 0.1):
         t_pct : float, optional, default=0.1
             Threshold parameter (T) for Six Peaks fitness function.
         """
-        self.t_pct: float = t_pct
         self.prob_type: str = "discrete"
+        self.t_pct: float = t_pct
 
         if not (0 <= self.t_pct <= 1):
             raise ValueError(f"threshold_pct must be between 0 and 1, got {self.t_pct}.")
diff --git a/src/mlrose_ky/fitness/travelling_salesperson.py b/src/mlrose_ky/fitness/travelling_sales.py
similarity index 100%
rename from src/mlrose_ky/fitness/travelling_salesperson.py
rename to src/mlrose_ky/fitness/travelling_sales.py
index 2cd0b9b8..55276087 100644
--- a/src/mlrose_ky/fitness/travelling_salesperson.py
+++ b/src/mlrose_ky/fitness/travelling_sales.py
@@ -89,33 +89,6 @@ def __init__(self, coords: list[tuple] = None, distances: list[tuple] = None):
                 self.distance_matrix[u, v] = d
                 self.distance_matrix[v, u] = d
 
-    def evaluate(self, state: np.ndarray) -> float:
-        """
-        Evaluate the fitness of a state vector.
-
-        Parameters
-        ----------
-        state : np.ndarray
-            State array for evaluation. Each integer between 0 and (len(state) - 1), inclusive must appear exactly once in the array.
-
-        Returns
-        -------
-        fitness : float
-            Value of fitness function. Returns np.inf if travel between two consecutive nodes on the tour is not possible.
-        """
-        # Validation checks on the state array
-        if self.is_coords and len(state) != len(self.coords):
-            raise ValueError("state must have the same length as coords.")
-        if not len(state) == len(set(state)):
-            raise ValueError("Each node must appear exactly once in state.")
-        if min(state) < 0:
-            raise ValueError("All elements of state must be non-negative integers.")
-        if max(state) >= len(state):
-            raise ValueError("All elements of state must be less than len(state).")
-
-        # Calculate and return the fitness of the state
-        return float(self.calculate_fitness(state))
-
     def __calculate_fitness_by_coords(self, state: np.ndarray) -> float:
         """
         Calculate fitness based on coordinates.
@@ -170,6 +143,33 @@ def __calculate_fitness_by_distance(self, state: np.ndarray) -> float:
 
         return float(fitness)
 
+    def evaluate(self, state: np.ndarray) -> float:
+        """
+        Evaluate the fitness of a state vector.
+
+        Parameters
+        ----------
+        state : np.ndarray
+            State array for evaluation. Each integer between 0 and (len(state) - 1), inclusive must appear exactly once in the array.
+
+        Returns
+        -------
+        fitness : float
+            Value of fitness function. Returns np.inf if travel between two consecutive nodes on the tour is not possible.
+        """
+        # Validation checks on the state array
+        if self.is_coords and len(state) != len(self.coords):
+            raise ValueError("state must have the same length as coords.")
+        if not len(state) == len(set(state)):
+            raise ValueError("Each node must appear exactly once in state.")
+        if min(state) < 0:
+            raise ValueError("All elements of state must be non-negative integers.")
+        if max(state) >= len(state):
+            raise ValueError("All elements of state must be less than len(state).")
+
+        # Calculate and return the fitness of the state
+        return float(self.calculate_fitness(state))
+
     def get_prob_type(self) -> str:
         """
         Return the problem type.
diff --git a/src/mlrose_ky/generators/knapsack_generator.py b/src/mlrose_ky/generators/knapsack_generator.py
index a7c5cd85..e3caa1e1 100644
--- a/src/mlrose_ky/generators/knapsack_generator.py
+++ b/src/mlrose_ky/generators/knapsack_generator.py
@@ -68,8 +68,8 @@ def generate(
 
         np.random.seed(seed)
 
-        weights = 1 + np.random.randint(max_weight_per_item, size=number_of_item_types)
-        values = 1 + np.random.randint(max_value_per_item, size=number_of_item_types)
+        weights = np.random.randint(1, max_weight_per_item + 1, size=number_of_item_types).tolist()
+        values = np.random.randint(1, max_value_per_item + 1, size=number_of_item_types).tolist()
 
         return KnapsackOpt(
             length=number_of_item_types,
diff --git a/src/mlrose_ky/gridsearch/grid_search_mixin.py b/src/mlrose_ky/gridsearch/grid_search_mixin.py
index b05e6a65..52bc07a3 100644
--- a/src/mlrose_ky/gridsearch/grid_search_mixin.py
+++ b/src/mlrose_ky/gridsearch/grid_search_mixin.py
@@ -40,7 +40,7 @@ def __init__(self, scorer_method: Callable = None):
         self._params: inspect.Signature = inspect.signature(self._scorer_method)
         self._get_y_argmax: bool = False
 
-    def perform_grid_search(
+    def _perform_grid_search(
         self, classifier: Any, x_train: np.ndarray, y_train: np.ndarray, cv: int, parameters: dict, n_jobs: int = 1, verbose: bool = False
     ) -> skms.GridSearchCV:
         """
@@ -58,10 +58,10 @@ def perform_grid_search(
             Number of cross-validation folds.
         parameters : dict
             Dictionary with parameters names as keys and lists of parameter settings to try as values.
-        n_jobs : int, optional
-            Number of jobs to run in parallel. Defaults to 1.
-        verbose : bool, optional
-            Whether to display verbose output during grid search. Defaults to False.
+        n_jobs : int, optional, default=1
+            Number of jobs to run in parallel.
+        verbose : bool, optional, default=1
+            Whether to display verbose output during grid search.
 
         Returns
         -------
diff --git a/src/mlrose_ky/neural/__init__.py b/src/mlrose_ky/neural/__init__.py
index a3b1108f..c6edf441 100644
--- a/src/mlrose_ky/neural/__init__.py
+++ b/src/mlrose_ky/neural/__init__.py
@@ -3,10 +3,13 @@
 # Authors: Genevieve Hayes (modified by Andrew Rollings, Kyle Nakamura)
 # License: BSD 3-clause
 
-from .activation import identity, relu, leaky_relu, sigmoid, softmax, tanh
-from .fitness.network_weights import NetworkWeights
 from .linear_regression import LinearRegression
 from .logistic_regression import LogisticRegression
 from .neural_network import NeuralNetwork
 from .nn_classifier import NNClassifier
+
+from .activation import identity, relu, leaky_relu, sigmoid, softmax, tanh
+
+from .fitness.network_weights import NetworkWeights
+
 from .utils import flatten_weights, unflatten_weights
diff --git a/src/mlrose_ky/opt_probs/opt_prob.py b/src/mlrose_ky/opt_probs/_opt_prob.py
similarity index 97%
rename from src/mlrose_ky/opt_probs/opt_prob.py
rename to src/mlrose_ky/opt_probs/_opt_prob.py
index 550063ee..b904d036 100644
--- a/src/mlrose_ky/opt_probs/opt_prob.py
+++ b/src/mlrose_ky/opt_probs/_opt_prob.py
@@ -113,7 +113,7 @@ def eval_fitness(self, state: np.ndarray) -> float:
         self.fitness_evaluations += 1
         return fitness
 
-    def eval_mate_probs(self):
+    def eval_mate_probs(self) -> None:
         """Calculate the probability of each member of the population reproducing."""
         pop_fitness = np.copy(self.pop_fitness)
 
@@ -210,7 +210,7 @@ def get_state(self) -> np.ndarray:
         """
         return self.state
 
-    def set_population(self, new_population: np.ndarray):
+    def set_population(self, new_population: np.ndarray) -> None:
         """Set a new population and evaluate its fitness.
 
         Parameters
@@ -221,11 +221,11 @@ def set_population(self, new_population: np.ndarray):
         self.population = new_population
         self.evaluate_population_fitness()
 
-    def evaluate_population_fitness(self):
+    def evaluate_population_fitness(self) -> None:
         """Evaluate the fitness of the current population."""
         self.pop_fitness = np.array([self.eval_fitness(indiv) for indiv in self.population])
 
-    def set_state(self, new_state: np.ndarray):
+    def set_state(self, new_state: np.ndarray) -> None:
         """Set a new state vector and evaluate its fitness.
 
         Parameters
diff --git a/src/mlrose_ky/opt_probs/continuous_opt.py b/src/mlrose_ky/opt_probs/continuous_opt.py
index f95bf0c1..6a77d4a9 100644
--- a/src/mlrose_ky/opt_probs/continuous_opt.py
+++ b/src/mlrose_ky/opt_probs/continuous_opt.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from mlrose_ky.opt_probs.opt_prob import _OptProb
+from mlrose_ky.opt_probs._opt_prob import _OptProb
 
 
 # Authors: Genevieve Hayes (modified by Andrew Rollings, Kyle Nakamura)
@@ -63,10 +63,10 @@ def __init__(self, length: int, fitness_fn: Any, maximize: bool = True, min_val:
         if (max_val - min_val) < step:
             raise ValueError(f"step size must be less than (max_val - min_val).")
 
+        self.prob_type: str = "continuous"
         self.min_val: float = min_val
         self.max_val: float = max_val
         self.step: float = step
-        self.prob_type: str = "continuous"
 
     def calculate_updates(self) -> list:
         """Calculate gradient descent updates.
diff --git a/src/mlrose_ky/opt_probs/discrete_opt.py b/src/mlrose_ky/opt_probs/discrete_opt.py
index 153b2362..4ffe9558 100644
--- a/src/mlrose_ky/opt_probs/discrete_opt.py
+++ b/src/mlrose_ky/opt_probs/discrete_opt.py
@@ -10,9 +10,9 @@
 from scipy.sparse.csgraph import minimum_spanning_tree, depth_first_tree
 from sklearn.metrics import mutual_info_score
 
-from mlrose_ky.algorithms.crossovers import UniformCrossover, TSPCrossover
+from mlrose_ky.algorithms.crossovers import UniformCrossOver, TSPCrossOver
 from mlrose_ky.algorithms.mutators import SwapMutator
-from mlrose_ky.opt_probs.opt_prob import _OptProb
+from mlrose_ky.opt_probs._opt_prob import _OptProb
 
 
 class DiscreteOpt(_OptProb):
@@ -34,8 +34,8 @@ class DiscreteOpt(_OptProb):
         Number of unique values that each element in the state vector can take.
         Assumes values are integers in the range 0 to (max_val - 1), inclusive.
 
-    crossover : UniformCrossover | TSPCrossover, default=None
-        Crossover operation used for reproduction. If None, defaults to `UniformCrossover`.
+    crossover : UniformCrossOver | TSPCrossOver, default=None
+        Crossover operation used for reproduction. If None, defaults to `UniformCrossOver`.
 
     mutator : SwapMutator, default=None
         Mutation operation used for reproduction. If None, defaults to `SwapMutator`.
@@ -54,7 +54,7 @@ class DiscreteOpt(_OptProb):
         Problem type; always 'discrete' for this class.
     noise : float
         Noise factor for probability density estimation.
-    _crossover : UniformCrossover
+    _crossover : UniformCrossOver
         Crossover operation for reproduction.
     _mutator : SwapMutator
         Mutation operation for reproduction.
@@ -70,7 +70,7 @@ def __init__(
         fitness_fn: Any,
         maximize: bool = True,
         max_val: int = 2,
-        crossover: UniformCrossover | TSPCrossover = None,
+        crossover: UniformCrossOver | TSPCrossOver = None,
         mutator: "SwapMutator" = None,
     ):
         self._get_mutual_info_impl = self._get_mutual_info_slow
@@ -93,20 +93,20 @@ def __init__(
         else:
             self.max_val: int = max_val
 
+        self.prob_type: str = "discrete"
         self.keep_sample: np.ndarray = np.array([])
         self.node_probs: np.ndarray = np.zeros([self.length, self.max_val, self.max_val])
         self.parent_nodes: np.ndarray = np.array([])
         self.sample_order: list[int] = []
-        self.prob_type: str = "discrete"
         self.noise: float = 0
 
-        self._crossover: UniformCrossover | TSPCrossover = UniformCrossover(self) if crossover is None else crossover
+        self._crossover: UniformCrossOver | TSPCrossOver = UniformCrossOver(self) if crossover is None else crossover
         self._mutator: SwapMutator = SwapMutator(self) if mutator is None else mutator
 
         self._mut_mask: np.ndarray | None = None
         self._mut_inf: np.ndarray | None = None
 
-    def eval_node_probs(self):
+    def eval_node_probs(self) -> None:
         """Update probability density estimates."""
         mutual_info = self._get_mutual_info_impl()
 
@@ -143,7 +143,7 @@ def eval_node_probs(self):
         self.node_probs = probs
         self.parent_nodes = parent
 
-    def set_mimic_fast_mode(self, fast_mode: bool):
+    def set_mimic_fast_mode(self, fast_mode: bool) -> None:
         """Enable or disable MIMIC fast mode."""
         if fast_mode:
             mut_mask = np.zeros([self.length, self.length], dtype=bool)
@@ -214,7 +214,7 @@ def _get_mutual_info_fast(self) -> np.ndarray:
 
         return mutual_info
 
-    def find_neighbors(self):
+    def find_neighbors(self) -> None:
         """Find all neighbors of the current state."""
         self.neighbors = []
 
@@ -233,7 +233,7 @@ def find_neighbors(self):
                     neighbor[i] = j
                     self.neighbors.append(neighbor)
 
-    def find_sample_order(self):
+    def find_sample_order(self) -> None:
         """Determine order in which to generate sample vector elements."""
         sample_order = []
         last = [0]
@@ -253,7 +253,7 @@ def find_sample_order(self):
 
         self.sample_order = sample_order
 
-    def find_top_pct(self, keep_pct: float):
+    def find_top_pct(self, keep_pct: float) -> None:
         """Select samples with fitness in the top keep_pct percentile.
 
         Parameters
@@ -318,7 +318,7 @@ def random_neighbor(self) -> np.ndarray:
 
         return neighbor
 
-    def random_pop(self, pop_size: int):
+    def random_pop(self, pop_size: int) -> None:
         """Create a population of random state vectors.
 
         Parameters
@@ -367,7 +367,7 @@ def reproduce(self, parent_1: np.ndarray, parent_2: np.ndarray, mutation_prob: f
         child = self._crossover.mate(parent_1, parent_2)
         return self._mutator.mutate(child, mutation_prob)
 
-    def reset(self):
+    def reset(self) -> None:
         """Set the current state vector to a random value and get its fitness."""
         self.state = self.random()
         self.fitness = self.eval_fitness(self.state)
diff --git a/src/mlrose_ky/opt_probs/flip_flop_opt.py b/src/mlrose_ky/opt_probs/flip_flop_opt.py
index 1fe4a07d..bfdcbcfb 100644
--- a/src/mlrose_ky/opt_probs/flip_flop_opt.py
+++ b/src/mlrose_ky/opt_probs/flip_flop_opt.py
@@ -7,7 +7,7 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers import OnePointCrossover
+from mlrose_ky.algorithms.crossovers import OnePointCrossOver
 from mlrose_ky.algorithms.mutators import ChangeOneMutator
 from mlrose_ky.fitness import FlipFlop
 from mlrose_ky.opt_probs.discrete_opt import DiscreteOpt
@@ -27,8 +27,8 @@ class FlipFlopOpt(DiscreteOpt):
     maximize : bool, default=True
         Whether to maximize the fitness function. Set :code:`False` for minimization problems.
 
-    crossover : OnePointCrossover, default=None
-        Crossover operation used for reproduction. If None, defaults to `OnePointCrossover`.
+    crossover : OnePointCrossOver, default=None
+        Crossover operation used for reproduction. If None, defaults to `OnePointCrossOver`.
 
     mutator : ChangeOneMutator, default=None
         Mutation operation used for reproduction. If None, defaults to `ChangeOneMutator`.
@@ -56,7 +56,7 @@ def __init__(
         length: int = None,
         fitness_fn: Any = None,
         maximize: bool = True,
-        crossover: "OnePointCrossover" = None,
+        crossover: "OnePointCrossOver" = None,
         mutator: "ChangeOneMutator" = None,
     ):
         if (fitness_fn is None) and (length is None):
@@ -71,7 +71,7 @@ def __init__(
             fitness_fn = FlipFlop()
 
         self.max_val: int = 2
-        crossover = OnePointCrossover(self) if crossover is None else crossover
+        crossover = OnePointCrossOver(self) if crossover is None else crossover
         mutator = ChangeOneMutator(self) if mutator is None else mutator
 
         super().__init__(length, fitness_fn, maximize, crossover=crossover, mutator=mutator)
diff --git a/src/mlrose_ky/opt_probs/knapsack_opt.py b/src/mlrose_ky/opt_probs/knapsack_opt.py
index 4304bfc6..3fbeecb8 100644
--- a/src/mlrose_ky/opt_probs/knapsack_opt.py
+++ b/src/mlrose_ky/opt_probs/knapsack_opt.py
@@ -5,7 +5,7 @@
 
 from typing import Any
 
-from mlrose_ky.algorithms.crossovers import UniformCrossover
+from mlrose_ky.algorithms.crossovers import UniformCrossOver
 from mlrose_ky.algorithms.mutators import ChangeOneMutator
 from mlrose_ky.fitness.knapsack import Knapsack
 from mlrose_ky.opt_probs.discrete_opt import DiscreteOpt
@@ -37,8 +37,8 @@ class KnapsackOpt(DiscreteOpt):
     max_weight_pct : float, default=0.35
         Maximum allowable weight as a percentage of the total weight.
 
-    crossover : UniformCrossover, default=None
-        Crossover operation used for reproduction. If None, defaults to `UniformCrossover`.
+    crossover : UniformCrossOver, default=None
+        Crossover operation used for reproduction. If None, defaults to `UniformCrossOver`.
 
     mutator : ChangeOneMutator, default=None
         Mutation operation used for reproduction. If None, defaults to `ChangeOneMutator`.
@@ -67,7 +67,7 @@ def __init__(
         weights: list[float] = None,
         values: list[float] = None,
         max_weight_pct: float = 0.35,
-        crossover: "UniformCrossover" = None,
+        crossover: "UniformCrossOver" = None,
         mutator: "ChangeOneMutator" = None,
         multiply_by_max_item_count: bool = False,
     ):
@@ -94,7 +94,7 @@ def __init__(
                 multiply_by_max_item_count=multiply_by_max_item_count,
             )
 
-        crossover = UniformCrossover(self) if crossover is None else crossover
+        crossover = UniformCrossOver(self) if crossover is None else crossover
         mutator = ChangeOneMutator(self) if mutator is None else mutator
 
         super().__init__(length, fitness_fn, maximize, max_val, crossover, mutator)
diff --git a/src/mlrose_ky/opt_probs/max_k_color_opt.py b/src/mlrose_ky/opt_probs/max_k_color_opt.py
index 9139c0b5..d891ae86 100644
--- a/src/mlrose_ky/opt_probs/max_k_color_opt.py
+++ b/src/mlrose_ky/opt_probs/max_k_color_opt.py
@@ -8,7 +8,7 @@
 import networkx as nx
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers import UniformCrossover
+from mlrose_ky.algorithms.crossovers import UniformCrossOver
 from mlrose_ky.algorithms.mutators import ChangeOneMutator
 from mlrose_ky.fitness import MaxKColor
 from mlrose_ky.opt_probs.discrete_opt import DiscreteOpt
@@ -34,8 +34,8 @@ class MaxKColorOpt(DiscreteOpt):
     max_colors : int, default=None
         Maximum number of colors to use for coloring the graph.
 
-    crossover : UniformCrossover, default=None
-        Crossover operation used for reproduction. If None, defaults to `UniformCrossover`.
+    crossover : UniformCrossOver, default=None
+        Crossover operation used for reproduction. If None, defaults to `UniformCrossOver`.
 
     mutator : ChangeOneMutator, default=None
         Mutation operation used for reproduction. If None, defaults to `ChangeOneMutator`.
@@ -68,7 +68,7 @@ def __init__(
         fitness_fn: Any = None,
         maximize: bool = False,
         max_colors: int = None,
-        crossover: "UniformCrossover" = None,
+        crossover: "UniformCrossOver" = None,
         mutator: "ChangeOneMutator" = None,
         source_graph: nx.Graph = None,
     ):
@@ -126,7 +126,7 @@ def __init__(
         self.max_val: int = max_colors
 
         # Use default crossover and mutator if none are provided
-        crossover = UniformCrossover(self) if crossover is None else crossover
+        crossover = UniformCrossOver(self) if crossover is None else crossover
         mutator = ChangeOneMutator(self) if mutator is None else mutator
         super().__init__(length, fitness_fn, maximize, max_colors, crossover, mutator)
 
diff --git a/src/mlrose_ky/opt_probs/queens_opt.py b/src/mlrose_ky/opt_probs/queens_opt.py
index 56a1d2cf..53bb490b 100644
--- a/src/mlrose_ky/opt_probs/queens_opt.py
+++ b/src/mlrose_ky/opt_probs/queens_opt.py
@@ -7,7 +7,7 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers import UniformCrossover
+from mlrose_ky.algorithms.crossovers import UniformCrossOver
 from mlrose_ky.algorithms.mutators import ChangeOneMutator
 from mlrose_ky.fitness.queens import Queens
 from mlrose_ky.opt_probs.discrete_opt import DiscreteOpt
@@ -27,8 +27,8 @@ class QueensOpt(DiscreteOpt):
     maximize : bool, default=False
         Whether to maximize the fitness function. Set :code:`False` for minimization problems.
 
-    crossover : UniformCrossover, default=None
-        Crossover operation used for reproduction. If None, defaults to `UniformCrossover`.
+    crossover : UniformCrossOver, default=None
+        Crossover operation used for reproduction. If None, defaults to `UniformCrossOver`.
 
     mutator : ChangeOneMutator, default=None
         Mutation operation used for reproduction. If None, defaults to `ChangeOneMutator`.
@@ -53,7 +53,7 @@ def __init__(
         length: int = None,
         fitness_fn: Any = None,
         maximize: bool = False,
-        crossover: "UniformCrossover" = None,
+        crossover: "UniformCrossOver" = None,
         mutator: "ChangeOneMutator" = None,
     ):
         # Ensure that either fitness_fn or length is provided
@@ -77,7 +77,7 @@ def __init__(
         self.max_val: int = length
 
         # Use default crossover and mutator if none are provided
-        crossover = UniformCrossover(self) if crossover is None else crossover
+        crossover = UniformCrossOver(self) if crossover is None else crossover
         mutator = ChangeOneMutator(self) if mutator is None else mutator
         super().__init__(length, fitness_fn, maximize, length, crossover, mutator)
 
diff --git a/src/mlrose_ky/opt_probs/tsp_opt.py b/src/mlrose_ky/opt_probs/tsp_opt.py
index e0c44504..29c4b82c 100644
--- a/src/mlrose_ky/opt_probs/tsp_opt.py
+++ b/src/mlrose_ky/opt_probs/tsp_opt.py
@@ -7,7 +7,7 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers import TSPCrossover
+from mlrose_ky.algorithms.crossovers import TSPCrossOver
 from mlrose_ky.algorithms.mutators import SwapMutator
 from mlrose_ky.fitness import TravellingSales
 from mlrose_ky.opt_probs.discrete_opt import DiscreteOpt
@@ -44,8 +44,8 @@ def __init__(
         length: int = None,
         fitness_fn: Any = None,
         maximize: bool = False,
-        coords: list[tuple[float, float]] = None,
-        distances: list[tuple[int, int, float]] = None,
+        coords: list[tuple] = None,
+        distances: list[tuple] = None,
         source_graph: Any = None,
     ):
         # Ensure that at least one of fitness_fn, coords, or distances is provided
@@ -56,8 +56,8 @@ def __init__(
         if fitness_fn is None:
             fitness_fn = TravellingSales(coords=coords, distances=distances)
 
-        self.distances: list[tuple[int, int, float]] | None = distances
-        self.coords: list[tuple[float, float]] | None = coords
+        self.distances: list[tuple] | None = distances
+        self.coords: list[tuple] | None = coords
 
         # If length is not provided, infer it from coords or distances
         if length is None:
@@ -69,8 +69,8 @@ def __init__(
 
         self.length: int = length
 
-        # Initialize the parent class with the TSPCrossover and SwapMutator
-        super().__init__(length, fitness_fn, maximize, max_val=length, crossover=TSPCrossover(self), mutator=SwapMutator(self))
+        # Initialize the parent class with the TSPCrossOver and SwapMutator
+        super().__init__(length, fitness_fn, maximize, max_val=length, crossover=TSPCrossOver(self), mutator=SwapMutator(self))
 
         # Ensure that the fitness function type is 'tsp'
         if self.fitness_fn.get_prob_type() != "tsp":
diff --git a/src/mlrose_ky/runners/__init__.py b/src/mlrose_ky/runners/__init__.py
index 7d3d991a..790b3bf6 100644
--- a/src/mlrose_ky/runners/__init__.py
+++ b/src/mlrose_ky/runners/__init__.py
@@ -3,11 +3,13 @@
 # Authors: Andrew Rollings (modified by Kyle Nakamura)
 # License: BSD 3-clause
 
-from ._nn_runner_base import _NNRunnerBase
 from .ga_runner import GARunner
 from .mimic_runner import MIMICRunner
 from .nngs_runner import NNGSRunner
 from .rhc_runner import RHCRunner
 from .sa_runner import SARunner
 from .skmlp_runner import SKMLPRunner
+
+from ._nn_runner_base import _NNRunnerBase
+
 from .utils import build_data_filename
diff --git a/src/mlrose_ky/runners/_nn_runner_base.py b/src/mlrose_ky/runners/_nn_runner_base.py
index e1879ee3..deaa523b 100644
--- a/src/mlrose_ky/runners/_nn_runner_base.py
+++ b/src/mlrose_ky/runners/_nn_runner_base.py
@@ -150,17 +150,6 @@ def __init__(
         self.cv_results_df: pd.DataFrame | None = None
         self.best_params: dict | None = None
 
-    def dynamic_runner_name(self) -> str:
-        """
-        Generates a dynamic name for the runner based on the class name and experiment name.
-
-        Returns
-        -------
-        str
-            The dynamic name for the runner.
-        """
-        return f"{self.__class__.__name__}_{self._experiment_name}"
-
     def run(self) -> tuple[pd.DataFrame | None, pd.DataFrame | None, pd.DataFrame | None, Any | None]:
         """
         Executes the runner, performing grid search and handling the results.
@@ -182,7 +171,7 @@ def run(self) -> tuple[pd.DataFrame | None, pd.DataFrame | None, pd.DataFrame |
                     search_results = pk.load(pickle_file)
             else:
                 run_start = time.perf_counter()
-                search_results = self.perform_grid_search(
+                search_results = self._perform_grid_search(
                     classifier=self.classifier,
                     parameters=self.grid_search_parameters,
                     x_train=self.x_train,
diff --git a/src/mlrose_ky/runners/_runner_base.py b/src/mlrose_ky/runners/_runner_base.py
index 65e934b2..6c070551 100644
--- a/src/mlrose_ky/runners/_runner_base.py
+++ b/src/mlrose_ky/runners/_runner_base.py
@@ -34,67 +34,23 @@ class _RunnerBase(ABC):
 
     Attributes
     ----------
-    _abort_flag : multiprocessing.Value
+    __abort : multiprocessing.Value
         Flag to signal abortion of the experiment.
-    _spawn_count : multiprocessing.Value
+    __spawn_count : multiprocessing.Value
         Tracks the number of spawned processes in parallel execution.
-    _replay_mode : multiprocessing.Value
+    __replay : multiprocessing.Value
         Indicates whether replay mode is active.
-    _original_sigint_handler : Any
+    __original_sigint_handler : Any
         Stores the original signal handler for Ctrl-C.
-    _sigint_params : tuple[int, Any] | None
+    __sigint_params : tuple[int, Any] | None
         Stores the signal and frame parameters when Ctrl-C is triggered.
     """
 
-    _abort_flag: multiprocessing.Value = multiprocessing.Value(ctypes.c_bool)
-    _spawn_count: multiprocessing.Value = multiprocessing.Value(ctypes.c_uint)
-    _replay_mode: multiprocessing.Value = multiprocessing.Value(ctypes.c_bool)
-    _original_sigint_handler: Any = None
-    _sigint_params: tuple[int, Any] | None = None
-
-    @classmethod
-    def get_runner_name(cls) -> str:
-        """Get a short name for the runner class."""
-        return get_short_name(cls)
-
-    def get_dynamic_runner_name(self) -> str:
-        """Get the dynamic name of the runner, if set, otherwise return the default runner name."""
-        dynamic_runner_name = self._dynamic_short_name or self.get_runner_name()
-
-        if not dynamic_runner_name:
-            raise ValueError("dynamic_runner_name is None")
-
-        return dynamic_runner_name
-
-    def set_dynamic_runner_name(self, name: str):
-        """Set a dynamic runner name."""
-        self._dynamic_short_name = name
-
-    @staticmethod
-    def _print_banner(text: str):
-        """Print a formatted banner for logging."""
-        logging.info("*" * len(text))
-        logging.info(text)
-        logging.info("*" * len(text))
-
-    @staticmethod
-    def _sanitize_value(value: Any) -> str:
-        """Sanitize a value for logging, handling different types appropriately."""
-        if isinstance(value, (tuple, list)):
-            sanitized_value = str(value)
-        elif isinstance(value, np.ndarray):
-            sanitized_value = str(list(value))
-        elif callable(value):
-            sanitized_value = get_short_name(value)
-        else:
-            sanitized_value = str(value)  # Handle non-callable types like floats, ints, etc.
-
-        return sanitized_value
-
-    @abstractmethod
-    def run(self):
-        """Abstract method to be implemented by subclasses."""
-        pass
+    __abort: multiprocessing.Value = multiprocessing.Value(ctypes.c_bool)
+    __spawn_count: multiprocessing.Value = multiprocessing.Value(ctypes.c_uint)
+    __replay: multiprocessing.Value = multiprocessing.Value(ctypes.c_bool)
+    __original_sigint_handler: Any = None
+    __sigint_params: tuple[int, Any] | None = None
 
     def __init__(
         self,
@@ -169,40 +125,84 @@ def __init__(
 
         self._increment_spawn_count()
 
+    @classmethod
+    def runner_name(cls) -> str:
+        """Get a short name for the runner class."""
+        return get_short_name(cls)
+
+    def dynamic_runner_name(self) -> str:
+        """Get the dynamic name of the runner, if set, otherwise return the default runner name."""
+        dynamic_runner_name = self._dynamic_short_name or self.runner_name()
+
+        if not dynamic_runner_name:
+            raise ValueError("dynamic_runner_name is None")
+
+        return dynamic_runner_name
+
+    def _set_dynamic_runner_name(self, name: str):
+        """Set a dynamic runner name."""
+        self._dynamic_short_name = name
+
+    @staticmethod
+    def _print_banner(text: str):
+        """Print a formatted banner for logging."""
+        logging.info("*" * len(text))
+        logging.info(text)
+        logging.info("*" * len(text))
+
+    @staticmethod
+    def _sanitize_value(value: Any) -> str:
+        """Sanitize a value for logging, handling different types appropriately."""
+        if isinstance(value, (tuple, list)):
+            sanitized_value = str(value)
+        elif isinstance(value, np.ndarray):
+            sanitized_value = str(list(value))
+        elif callable(value):
+            sanitized_value = get_short_name(value)
+        else:
+            sanitized_value = str(value)  # Handle non-callable types like floats, ints, etc.
+
+        return sanitized_value
+
+    @abstractmethod
+    def run(self):
+        """Abstract method to be implemented by subclasses."""
+        pass
+
     def _increment_spawn_count(self):
         """Increment the spawn count for the runner (used in parallel execution)."""
-        with self._spawn_count.get_lock():
-            self._spawn_count.value += 1
+        with self.__spawn_count.get_lock():
+            self.__spawn_count.value += 1
 
     def _decrement_spawn_count(self):
         """Decrement the spawn count for the runner."""
-        with self._spawn_count.get_lock():
-            self._spawn_count.value -= 1
+        with self.__spawn_count.get_lock():
+            self.__spawn_count.value -= 1
 
-    def get_spawn_count(self) -> int:
+    def _get_spawn_count(self) -> int:
         """Return the current spawn count and log it."""
-        self._print_banner(f"*** Spawn Count Remaining: {self._spawn_count.value} ***")
-        return self._spawn_count.value
+        self._print_banner(f"*** Spawn Count Remaining: {self.__spawn_count.value} ***")
+        return self.__spawn_count.value
 
     def abort(self):
         """Set the abort flag to signal that the runner should stop execution."""
         self._print_banner("*** ABORTING ***")
 
-        with self._abort_flag.get_lock():
-            self._abort_flag.value = True
+        with self.__abort.get_lock():
+            self.__abort.value = True
 
     def has_aborted(self) -> bool:
         """Return whether the abort flag has been set."""
-        return self._abort_flag.value
+        return self.__abort.value
 
     def set_replay_mode(self, value: bool = True):
         """Enable or disable replay mode, which reuses previous results."""
-        with self._replay_mode.get_lock():
-            self._replay_mode.value = value
+        with self.__replay.get_lock():
+            self.__replay.value = value
 
     def replay_mode(self) -> bool:
         """Check if replay mode is enabled."""
-        return self._replay_mode.value
+        return self.__replay.value
 
     def _setup(self):
         """Prepare the runner by clearing stats, setting up directories, and handling Ctrl-C interrupts."""
@@ -221,8 +221,8 @@ def _setup(self):
 
         # Set up Ctrl-C handler if necessary
         if self.override_ctrl_c_handler:
-            if self._original_sigint_handler is None:
-                self._original_sigint_handler = signal.getsignal(signal.SIGINT)
+            if self.__original_sigint_handler is None:
+                self.__original_sigint_handler = signal.getsignal(signal.SIGINT)
                 signal.signal(signal.SIGINT, self._ctrl_c_handler)
 
     def _ctrl_c_handler(self, sig: int, frame: Any):
@@ -237,7 +237,7 @@ def _ctrl_c_handler(self, sig: int, frame: Any):
             Current stack frame.
         """
         logging.info("Interrupted - saving progress so far")
-        self._sigint_params = (sig, frame)
+        self.__sigint_params = (sig, frame)
         self.abort()
 
     def _tear_down(self):
@@ -248,19 +248,19 @@ def _tear_down(self):
         try:
             # Restore the original Ctrl-C handler
             self._decrement_spawn_count()
-            if self._original_sigint_handler is not None:
-                signal.signal(signal.SIGINT, self._original_sigint_handler)
-                if self.has_aborted() and self.get_spawn_count() == 0:
-                    sig, frame = self._sigint_params
-                    self._original_sigint_handler(sig, frame)
+            if self.__original_sigint_handler is not None:
+                signal.signal(signal.SIGINT, self.__original_sigint_handler)
+                if self.has_aborted() and self._get_spawn_count() == 0:
+                    sig, frame = self.__sigint_params
+                    self.__original_sigint_handler(sig, frame)
         except (ValueError, TypeError, AttributeError, Exception) as e:
             logging.error(f"Problem restoring SIGINT handler: {e}")
 
-    def log_current_argument(self, arg_name: str, arg_value: Any):
+    def _log_current_argument(self, arg_name: str, arg_value: Any):
         """Log the current argument passed to the algorithm."""
         self._current_logged_algorithm_args[arg_name] = arg_value
 
-    def run_experiment(self, algorithm: Any, **kwargs: Any) -> tuple[pd.DataFrame | None, pd.DataFrame | None]:
+    def run_experiment_(self, algorithm: Any, **kwargs: Any) -> tuple[pd.DataFrame | None, pd.DataFrame | None]:
         """
         Execute the experiment with the provided algorithm and log the results.
 
@@ -285,7 +285,7 @@ def run_experiment(self, algorithm: Any, **kwargs: Any) -> tuple[pd.DataFrame |
 
         value_sets = list(itertools.product(*values))
 
-        logging.info(f"Running {self.get_dynamic_runner_name()}")
+        logging.info(f"Running {self.dynamic_runner_name()}")
         run_start = time.perf_counter()
 
         for value_set in value_sets:
@@ -380,7 +380,7 @@ def _get_pickle_filename_root(self, name: str) -> str:
         """Generate the root filename for the pickle file based on experiment metadata."""
         return build_data_filename(
             output_directory=self._output_directory,
-            runner_name=self.get_dynamic_runner_name(),
+            runner_name=self.dynamic_runner_name(),
             experiment_name=self._experiment_name,
             df_name=name,
         )
@@ -490,14 +490,14 @@ def _invoke_algorithm(
         kwargs = {k: v for k, v in total_args.items() if k in valid_args}
 
         # Reset the problem instance and run the algorithm
-        self.start_run_timing()
+        self._start_run_timing()
         problem.reset()
         result = algorithm(
             problem=problem,
             max_attempts=max_attempts,
             curve=curve,
             random_state=self.seed,
-            state_fitness_callback=self.save_state,
+            state_fitness_callback=self._save_state,
             callback_user_info=user_info,
             **kwargs,
         )
@@ -507,7 +507,7 @@ def _invoke_algorithm(
 
         return result
 
-    def start_run_timing(self):
+    def _start_run_timing(self):
         """Start timing the experiment's execution."""
         self._run_start_time = time.perf_counter()
 
@@ -543,7 +543,7 @@ def _create_curve_stat(
 
         return curve_stat
 
-    def save_state(
+    def _save_state(
         self,
         iteration: int,
         state: Any,
@@ -598,7 +598,7 @@ def save_state(
             logging.debug(data_desc)
 
         logging.debug(
-            f"runner_name:[{self.get_dynamic_runner_name()}], experiment_name:[{self._experiment_name}], "
+            f"runner_name:[{self.dynamic_runner_name()}], experiment_name:[{self._experiment_name}], "
             + ("" if attempt is None else f"attempt:[{attempt}], ")
             + f"iteration:[{iteration}], done:[{done}], "
             f"time:[{t:.2f}], fitness:[{fitness:.4f}]"
diff --git a/src/mlrose_ky/runners/ga_runner.py b/src/mlrose_ky/runners/ga_runner.py
index 5095f3bc..9efb7659 100644
--- a/src/mlrose_ky/runners/ga_runner.py
+++ b/src/mlrose_ky/runners/ga_runner.py
@@ -117,7 +117,7 @@ def run(self) -> tuple[pd.DataFrame, pd.DataFrame]:
         tuple
             A tuple containing two DataFrames: run statistics and run curves
         """
-        return super().run_experiment(
+        return super().run_experiment_(
             algorithm=mlrose_ky.genetic_alg,
             pop_size=("Population Size", self.population_sizes),
             mutation_prob=("Mutation Rate", self.mutation_rates),
diff --git a/src/mlrose_ky/runners/mimic_runner.py b/src/mlrose_ky/runners/mimic_runner.py
index 513b873e..e4f93783 100644
--- a/src/mlrose_ky/runners/mimic_runner.py
+++ b/src/mlrose_ky/runners/mimic_runner.py
@@ -112,7 +112,7 @@ def _setup(self):
         super()._setup()
 
         if self._use_fast_mimic is not None:
-            self.log_current_argument("use_fast_mimic", self._use_fast_mimic)
+            self._log_current_argument("use_fast_mimic", self._use_fast_mimic)
 
     def run(self) -> tuple[pd.DataFrame | None, pd.DataFrame | None]:
         """
@@ -126,7 +126,7 @@ def run(self) -> tuple[pd.DataFrame | None, pd.DataFrame | None]:
         tuple
             A tuple containing two DataFrames: run statistics and run curves
         """
-        return super().run_experiment(
+        return super().run_experiment_(
             algorithm=mlrose_ky.mimic,
             pop_size=("Population Size", self.population_sizes),
             keep_pct=("Keep Percent", self.keep_percent_list),
diff --git a/src/mlrose_ky/runners/nngs_runner.py b/src/mlrose_ky/runners/nngs_runner.py
index 752ab464..c394f6e0 100644
--- a/src/mlrose_ky/runners/nngs_runner.py
+++ b/src/mlrose_ky/runners/nngs_runner.py
@@ -157,7 +157,7 @@ def __init__(
         )
 
         # Update short name based on the algorithm
-        self.set_dynamic_runner_name(f"{get_short_name(self)}_{get_short_name(algorithm)}")
+        self._set_dynamic_runner_name(f"{get_short_name(self)}_{get_short_name(algorithm)}")
 
     def run_one_experiment_(self, algorithm: Any, total_args: dict, **params: dict) -> tuple | None:
         """
diff --git a/src/mlrose_ky/runners/rhc_runner.py b/src/mlrose_ky/runners/rhc_runner.py
index ad3ac06a..898cac88 100644
--- a/src/mlrose_ky/runners/rhc_runner.py
+++ b/src/mlrose_ky/runners/rhc_runner.py
@@ -97,4 +97,4 @@ def run(self) -> tuple[pd.DataFrame | None, pd.DataFrame | None]:
         tuple
             A tuple containing two DataFrames: run statistics and run curves.
         """
-        return super().run_experiment(algorithm=mlrose_ky.random_hill_climb, restarts=("Restarts", self.restart_list))
+        return super().run_experiment_(algorithm=mlrose_ky.random_hill_climb, restarts=("Restarts", self.restart_list))
diff --git a/src/mlrose_ky/runners/sa_runner.py b/src/mlrose_ky/runners/sa_runner.py
index a0fdf2f7..ba0679e7 100644
--- a/src/mlrose_ky/runners/sa_runner.py
+++ b/src/mlrose_ky/runners/sa_runner.py
@@ -123,4 +123,4 @@ def run(self) -> tuple[pd.DataFrame | None, pd.DataFrame | None]:
         temperatures = (
             self.temperature_list if self.use_raw_temp else [d(init_temp=t) for t in self.temperature_list for d in self.decay_list]
         )
-        return super().run_experiment(algorithm=mlrose_ky.simulated_annealing, schedule=("Temperature", temperatures))
+        return super().run_experiment_(algorithm=mlrose_ky.simulated_annealing, schedule=("Temperature", temperatures))
diff --git a/src/mlrose_ky/runners/skmlp_runner.py b/src/mlrose_ky/runners/skmlp_runner.py
index 6c6bc397..c8afc8e6 100644
--- a/src/mlrose_ky/runners/skmlp_runner.py
+++ b/src/mlrose_ky/runners/skmlp_runner.py
@@ -61,7 +61,7 @@ def __init__(self, runner: "SKMLPRunner", **kwargs):
             mlp_kwargs = {k: v for k, v in kwargs.items() if k in valid_args}
 
             self.mlp: MLPClassifier = MLPClassifier(**mlp_kwargs)
-            self.state_callback = self.runner.save_state
+            self.state_callback = self.runner._save_state
             self.fit_started_: bool = False
             self.user_info_: list[tuple[str, Any]] | None = None
             self.kwargs_: dict[str, Any] = kwargs
@@ -131,7 +131,7 @@ def fit(self, x_train: np.ndarray, y_train: np.ndarray = None) -> MLPClassifier:
                 The trained model.
             """
             self.fit_started_ = True
-            self.runner.start_run_timing()
+            self.runner._start_run_timing()
 
             # Make initial callback
             self._invoke_runner_callback()
@@ -258,7 +258,7 @@ def _invoke_runner_callback(self):
             if self.user_info_ is None:
                 self.user_info_ = [(k, self.__dict__[k]) for k in self.kwargs_.keys() if hasattr(self, k)]
                 for k, v in self.user_info_:
-                    self.runner.log_current_argument(k, v)
+                    self.runner._log_current_argument(k, v)
 
             self.state_callback(
                 iteration=iterations,
diff --git a/tests/test_gridsearch.py b/tests/test_gridsearch.py
index b67d9d23..8eb643e2 100644
--- a/tests/test_gridsearch.py
+++ b/tests/test_gridsearch.py
@@ -66,7 +66,7 @@ def test_perform_grid_search(self, grid_search_mixin, sample_data, dummy_classif
         """Should perform grid search with a classifier and return GridSearchCV object"""
         X_train, X_test, y_train, y_test = sample_data
 
-        search_results = grid_search_mixin.perform_grid_search(
+        search_results = grid_search_mixin._perform_grid_search(
             classifier=dummy_classifier, x_train=X_train, y_train=y_train, cv=3, parameters=param_grid
         )
 
@@ -79,7 +79,7 @@ def test_perform_grid_search(self, grid_search_mixin, sample_data, dummy_classif
     def test_handle_empty_input(self, grid_search_mixin):
         """Should handle empty input data gracefully"""
         with pytest.raises(ValueError):
-            grid_search_mixin.perform_grid_search(
+            grid_search_mixin._perform_grid_search(
                 classifier=DummyClassifier(), x_train=np.array([]), y_train=np.array([]), cv=3, parameters={"strategy": ["most_frequent"]}
             )
 
@@ -172,7 +172,7 @@ def test_perform_grid_search_with_verbose(self, grid_search_mixin, sample_data,
         X_train, X_test, y_train, y_test = sample_data
 
         # Perform grid search with verbose output enabled
-        search_results = grid_search_mixin.perform_grid_search(
+        search_results = grid_search_mixin._perform_grid_search(
             dummy_classifier, X_train, y_train, cv=3, parameters=grid_search_parameters, verbose=True
         )
 
@@ -187,7 +187,7 @@ def test_perform_grid_search_with_multiple_jobs(self, grid_search_mixin, sample_
         X_train, X_test, y_train, y_test = sample_data
 
         # Perform grid search with n_jobs set to 2
-        search_results = grid_search_mixin.perform_grid_search(
+        search_results = grid_search_mixin._perform_grid_search(
             dummy_classifier, X_train, y_train, cv=3, parameters=grid_search_parameters, n_jobs=2
         )
 
diff --git a/tests/test_opt_probs/test_discrete_opt.py b/tests/test_opt_probs/test_discrete_opt.py
index cf36872d..cec6f501 100644
--- a/tests/test_opt_probs/test_discrete_opt.py
+++ b/tests/test_opt_probs/test_discrete_opt.py
@@ -8,7 +8,7 @@
 import mlrose_ky
 from mlrose_ky.opt_probs import DiscreteOpt
 from mlrose_ky.fitness import OneMax
-from mlrose_ky.algorithms import OnePointCrossover
+from mlrose_ky.algorithms import OnePointCrossOver
 
 
 class TestDiscreteOpt:
@@ -164,7 +164,7 @@ def test_reproduce_mut1_max2(self):
     def test_reproduce_mut1_max_gt2(self):
         """Test reproduce method when mutation_prob is 1 and max_val is greater than 2"""
         problem = DiscreteOpt(5, OneMax(), max_val=3)
-        problem._crossover = OnePointCrossover(problem)
+        problem._crossover = OnePointCrossOver(problem)
         father = np.zeros(5)
         mother = np.ones(5) * 2
         child = problem.reproduce(father, mother, mutation_prob=1)
diff --git a/tests/test_opt_probs/test_opt_prob.py b/tests/test_opt_probs/test_opt_prob.py
index 3c75e6de..bab16288 100644
--- a/tests/test_opt_probs/test_opt_prob.py
+++ b/tests/test_opt_probs/test_opt_prob.py
@@ -1,4 +1,4 @@
-"""Unit tests for opt_probs/opt_prob.py"""
+"""Unit tests for opt_probs/_opt_prob.py"""
 
 # Author: Genevieve Hayes (modified by Kyle Nakamura)
 # License: BSD 3-clause
@@ -6,7 +6,7 @@
 import numpy as np
 
 from mlrose_ky.fitness import OneMax
-from mlrose_ky.opt_probs.opt_prob import _OptProb
+from mlrose_ky.opt_probs._opt_prob import _OptProb
 
 
 class TestOptProb:
diff --git a/tests/test_runners/test_ga_runner.py b/tests/test_runners/test_ga_runner.py
index 00b315be..2e6fe4fb 100644
--- a/tests/test_runners/test_ga_runner.py
+++ b/tests/test_runners/test_ga_runner.py
@@ -102,7 +102,7 @@ def test_garunner_initialization_with_additional_kwargs(self, problem, runner_kw
         runner = GARunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == "ga"
+        assert runner.runner_name() == "ga"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]
diff --git a/tests/test_runners/test_mimic_runner.py b/tests/test_runners/test_mimic_runner.py
index ae83007c..cedda520 100644
--- a/tests/test_runners/test_mimic_runner.py
+++ b/tests/test_runners/test_mimic_runner.py
@@ -97,7 +97,7 @@ def test_mimicrunner_initialization_with_additional_kwargs(self, problem, runner
         runner = MIMICRunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == "mimic"
+        assert runner.runner_name() == "mimic"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]
diff --git a/tests/test_runners/test_nn_runner_base.py b/tests/test_runners/test_nn_runner_base.py
index 7835e615..42168aed 100644
--- a/tests/test_runners/test_nn_runner_base.py
+++ b/tests/test_runners/test_nn_runner_base.py
@@ -55,7 +55,7 @@ def test_nn_runner_base_initialization(self):
         assert runner.verbose_grid_search is True
         assert runner.override_ctrl_c_handler is True
         assert runner.n_jobs == 1
-        assert runner._replay_mode.value is False
+        assert runner.replay_mode() is False
         assert runner.cv_results_df is None
         assert runner.best_params is None
 
@@ -96,7 +96,7 @@ def test_nn_runner_base_run_method(self):
 
         with (
             patch.object(runner, "_setup", return_value=None) as mock_setup,
-            patch.object(runner, "perform_grid_search", return_value=mock_grid_search_result) as mock_grid_search,
+            patch.object(runner, "_perform_grid_search", return_value=mock_grid_search_result) as mock_grid_search,
             patch.object(runner, "_tear_down", return_value=None) as mock_tear_down,
             patch.object(runner, "_print_banner", return_value=None) as mock_print_banner,
         ):
@@ -127,7 +127,7 @@ def test_nn_runner_base_teardown_removes_files(self):
                 output_directory="test_output",
             )
 
-            runner.get_runner_name = MagicMock(return_value="TestRunner")
+            runner.runner_name = MagicMock(return_value="TestRunner")
             runner.best_params = {"param1": 0.1, "param2": 1}
             runner._output_directory = "test_output"
             runner.replay_mode = MagicMock(return_value=False)
diff --git a/tests/test_runners/test_rhc_runner.py b/tests/test_runners/test_rhc_runner.py
index 6f2a7a2d..155751c0 100644
--- a/tests/test_runners/test_rhc_runner.py
+++ b/tests/test_runners/test_rhc_runner.py
@@ -69,7 +69,7 @@ def test_rhc_runner_initialization_with_additional_kwargs(self, problem, runner_
         runner = RHCRunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == "rhc"
+        assert runner.runner_name() == "rhc"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]
diff --git a/tests/test_runners/test_runner_base.py b/tests/test_runners/test_runner_base.py
index 1e9f2ce6..509f640e 100644
--- a/tests/test_runners/test_runner_base.py
+++ b/tests/test_runners/test_runner_base.py
@@ -41,30 +41,30 @@ def _create_runner(**kwargs):
     def test_increment_spawn_count(self, test_runner):
         with patch("os.makedirs"), patch("os.path.exists", return_value=True):
             runner = test_runner()
-            initial_count = runner.get_spawn_count()
+            initial_count = runner._get_spawn_count()
             runner._increment_spawn_count()
 
-            assert runner.get_spawn_count() == initial_count + 1
+            assert runner._get_spawn_count() == initial_count + 1
 
     def test_decrement_spawn_count(self, test_runner):
         with patch("os.makedirs"), patch("os.path.exists", return_value=True):
             runner = test_runner()
             runner._increment_spawn_count()
-            initial_count = runner.get_spawn_count()
+            initial_count = runner._get_spawn_count()
             runner._decrement_spawn_count()
 
-            assert runner.get_spawn_count() == initial_count - 1
+            assert runner._get_spawn_count() == initial_count - 1
 
     def test_get_spawn_count(self, test_runner):
         with patch("os.makedirs"), patch("os.path.exists", return_value=True):
             runner = test_runner()
-            initial_spawn_count = runner.get_spawn_count()
+            initial_spawn_count = runner._get_spawn_count()
             runner._increment_spawn_count()
-            incremented_spawn_count = runner.get_spawn_count()
+            incremented_spawn_count = runner._get_spawn_count()
             assert incremented_spawn_count == initial_spawn_count + 1
 
             runner._decrement_spawn_count()
-            decremented_spawn_count = runner.get_spawn_count()
+            decremented_spawn_count = runner._get_spawn_count()
             assert decremented_spawn_count == initial_spawn_count
 
     def test_abort_sets_abort_flag(self, test_runner):
@@ -127,6 +127,6 @@ def test_log_current_argument(self, test_runner):
             runner = test_runner(seed=SEED, iteration_list=[0, 1, 2])
             arg_name = "test_arg"
             arg_value = "test_value"
-            runner.log_current_argument(arg_name, arg_value)
+            runner._log_current_argument(arg_name, arg_value)
 
             assert runner._current_logged_algorithm_args[arg_name] == arg_value
diff --git a/tests/test_runners/test_sa_runner.py b/tests/test_runners/test_sa_runner.py
index 7218dc2a..2aa15973 100644
--- a/tests/test_runners/test_sa_runner.py
+++ b/tests/test_runners/test_sa_runner.py
@@ -84,7 +84,7 @@ def test_sarunner_initialization_with_additional_kwargs(self, problem, runner_kw
         runner = SARunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == "sa"
+        assert runner.runner_name() == "sa"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]
diff --git a/tests/test_runners/test_skmlp_runner.py b/tests/test_runners/test_skmlp_runner.py
index 94d8a872..66cdae32 100644
--- a/tests/test_runners/test_skmlp_runner.py
+++ b/tests/test_runners/test_skmlp_runner.py
@@ -83,7 +83,7 @@ def test_dynamic_runner_name(self, runner_kwargs):
         """Test that the runner name is set dynamically based on the algorithm."""
         runner = SKMLPRunner(**runner_kwargs)
         expected_name = "skmlp"
-        assert runner.get_runner_name() == expected_name
+        assert runner.runner_name() == expected_name
 
     def test_grid_search_scorer_method(self, runner):
         """Test that the grid search scorer method is set correctly."""
@@ -102,6 +102,6 @@ def test_skmlp_runner_initialization_with_additional_kwargs(self, runner_kwargs)
         additional_kwargs = {"custom_arg": "custom_value"}
         runner = SKMLPRunner(**runner_kwargs, **additional_kwargs)
 
-        assert runner.get_runner_name() == "skmlp"
+        assert runner.runner_name() == "skmlp"
         assert runner.classifier.mlp.early_stopping == runner_kwargs["early_stopping"]
         assert runner.classifier.mlp.random_state == SEED